How to Extend 3D GBSM to Integrated Sensing and Communication Channel with Sharing Feature?

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), as standardized for 5G systems, addresses solely communication channels and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular focus on capturing the sharing feature of both channels, including shared scatterers, clusters, paths, and similar propagation param-eters, which have been experimentally verified in the literature. The proposed approach can be summarized as follows: Firstly, an ISAC channel model is proposed, where shared and non-shared components are superimposed for both communication and sensing. Secondly, sensing channel is characterized as a cascade of TX-target, radar cross section, and target-RX, with the introduction of a novel parameter S for shared target extraction. Finally, an ISAC channel implementation framework is proposed, allowing flexible configuration of sharing feature and the joint generation of communication and sensing channels. The proposed ISAC channel model can be compatible with the 3GPP standards and offers promising support for ISAC technology evaluation.

Channel Measurement, Modeling, and Simulation for 6G: A Survey and Tutorial

Technology research and standardization work of sixth generation (6G) has been carried out worldwide. Channel research is the prerequisite of 6G technology evaluation and optimization. This paper presents a survey and tutorial on channel measurement, modeling, and simulation for 6G. We first highlight the challenges of channel for 6G systems, including higher frequency band, extremely large antenna array, new technology combinations, and diverse application scenarios. A review of channel measurement and modeling for four possible 6G enabling technologies is then presented, i.e., terahertz communication, massive multiple-input multiple-output communication, joint communication and sensing, and reconfigurable intelligent surface. Finally, we introduce a 6G channel simulation platform and provide examples of its implementation. The goal of this paper is to help both professionals and non-professionals know the progress of 6G channel research, understand the 6G channel model, and use it for 6G simulation.

A Shared Cluster-based Stochastic Channel Model for Joint Communication and Sensing Systems

Joint communication and sensing (JCAS) has been recognized as a promising technology in the sixth generation (6G) communication. A realistic channel model is a prerequisite for designing JCAS systems. Most existing channel models independently generate the communication and sensing channels under the same framework. However, due to the multiplexing of hardware resources (e.g., antennas) and the same environment, signals enabled for communication and sensing may experience some shared propagation scatterers. This practical sharing feature necessities the joint generation of communication and sensing channels for realistic modeling, where the shared clusters (contributed by the shared scatterers) should be jointly reconstructed for both channels. In this paper, we first conduct communication and sensing channel measurements for an indoor scenario at 28 GHz. The power-angular-delay profiles (PADPs) of multipath components (MPCs) are obtained, and the shared scatterers by communication and sensing channels are intuitively observed. Then, a stochastic JCAS channel model is proposed to capture the sharing feature, where shared and non-shared clusters by the two channels are defined and superimposed. To extract those clusters from measured JCAS channels, a KPowerMeans-based joint clustering algorithm (KPM-JCA) is novelly introduced. Finally, stochastic channel characteristics are analyzed, and the practicality and controllability of the proposed model are validated based on the measurements and empirical simulations. The proposed model can realistically capture the sharing feature of JCAS channels, which is valuable for the design and deployment of JCAS systems.

Interpretable Almost-Matching-Exactly With Instrumental Variables

Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i.e., the presence of unobserved covariates linking treatments and outcomes. Instrumental Variables (IV) are commonly used to reduce the effects of unmeasured confounding. Existing methods for IV estimation either require strong parametric assumptions, use arbitrary distance metrics, or do not scale well to large datasets. We propose a matching framework for IV in the presence of observed categorical confounders that addresses these weaknesses. Our method first matches units exactly, and then consecutively drops variables to approximately match the remaining units on as many variables as possible. We show that our algorithm constructs better matches than other existing methods on simulated datasets, and we produce interesting results in an application to political canvassing.

Almost-Exact Matching with Replacement for Causal Inference

We aim to create the highest possible quality of treatment-control matches for categorical data in the potential outcomes framework. Matching methods are heavily used in the social sciences due to their interpretability, but most matching methods do not pass basic sanity checks: they fail when irrelevant variables are introduced, and tend to be either computationally slow or produce low-quality matches. The method proposed in this work aims to match units on a weighted Hamming distance, taking into account the relative importance of the covariates; the algorithm aims to match units on as many relevant variables as possible. To do this, the algorithm creates a hierarchy of covariate combinations on which to match (similar to downward closure), in the process solving an optimization problem for each unit in order to construct the optimal matches. The algorithm uses a single dynamic program to solve all of the optimization problems simultaneously. Notable advantages of our method over existing matching procedures are its high-quality matches, versatility in handling different data distributions that may have irrelevant variables, and ability to handle missing data by matching on as many available covariates as possible.